• Gulati, Jasmine
• Zhang, Xukai

Abstract

<jats:title>Abstract</jats:title><jats:p>The degradation of metallic systems under cyclic loading is prone to significant uncertainty. Uncertainty affects the reliability in the prediction of a residual lifetime and subsequent decisions regarding optimum maintenance schedules. This paper is concerned with addressing two main challenges faced in developing a credible reliability-based framework for the lifecycle management of fatigue-critical components. The first challenge is to construct a stochastic model that can adequately capture uncertainties observed in the crack growth histories. The second one involves presenting a computationally efficient strategy for solving the stochastic optimization associated with optimum maintenance scheduling. To that end, a Polynomial Chaos (PC) representation is constructed to propagate the uncertainty in the fatigue-induced crack growth process into the limit state functions using a database from a constant amplitude loading experiment. Secondly, an efficient and accurate optimization strategy based on the Gaussian process surrogate modeling is implemented to solve the stochastic optimization problem under the maximum probability of failure constraints. The sensitivity of the optimum solution to the different thresholds on the probability of failure is examined. The proposed framework provides a decision support tool for informed decision-making under uncertainty to mitigate fatigue failure.</jats:p>

Topics

• impedance spectroscopy
• experiment
• crack
• fatigue